Document content classification and alteration

ABSTRACT

A device may receive a document including text, images, and one or more embedded documents. The device may parse the document to identify a content segment in the document. The device may determine a context for the content segment, wherein the context includes at least one of an entity identified by the content segment, a semantic meaning of the content segment, or an object identified by the content segment. The device may classify the content segment using a content classification model and based on the context for the content segment. The device may selectively alter the content segment based at least in part on a set of alteration rules, to generate a modified document. The device may provide the modified document based on selectively altering the content segment.

BACKGROUND

A device may store documents that include information, such as text,images, video, audio, and/or the like. Some documents may includeembedded documents. For example, a textual document may include anembedded document that includes another textual document, a presentationdocument, an image document, a spreadsheet document, and/or the like.Document sanitization is a process in which sensitive information in adocument is removed or in some other way obscured. For example, beforepublishing scientific data regarding a medical study, a device may use alist of names of participants in the medical study to find-and-replace(e.g., by removing) names that are included in the list of names.Similarly, when a document is transferred from a first system (e.g., aninternal system of a company) to a second system (e.g., an externalsystem that provides access to clients or customers of the company), adevice may use a find-and-replace technique to remove pre-specifiedinformation from the document. For example, the device may removeinformation identifying company employees, information identifyingcompany financials, information identifying other customers of thecompany, and/or the like that is included in a pre-specified list ofinformation.

SUMMARY

According to some implementations, a method may include receiving, by adevice, a document including at least one of text, images, and one ormore embedded documents; parsing, by the device, the document toidentify a content segment in the document, wherein the content segmentis at least one of a text segment or an image, from at least one of thedocument or an embedded document of the one or more embedded documents,of the document; determining, by the device, a context for the contentsegment, wherein the context includes at least one of an entityidentified by the content segment, a semantic meaning of the contentsegment, or an object identified by the content segment; classifying, bythe device, the content segment using a content classification model andbased on the context for the content segment, wherein the contentsegment is classified into a first type that is proposed to be alteredor a second type that is proposed not to be altered; determining asuggested replacement for the first type of content segment; providing,for display via a user interface, information identifying the first typeof content segment, the second type of content segment, and thesuggested replacement; receiving, via the user interface, a set ofselections of at least a portion of the first type of content segment orthe second type of content segment or the suggested replacement;selectively altering, by the device, the content segment based at leastin part on a set of alteration rules and the set of selections, togenerate a modified document; and providing, by the device, the modifieddocument based on selectively altering the content segment; updating thecontent classification model based on the set of selections; and storingthe updated content classification model.

According to some implementations, a device may include one or morememories; and one or more processors, communicatively coupled to the oneor more memories, configured to: receive a document; parse the documentto identify a set of content segments in the document; determine a setof contexts for the set of content segments; classify the set of contentsegments using a content classification model and based on the set ofcontexts for the set of content segments, wherein a first subset of theset of content segments is classified into a type that is proposed to bealtered, and a second subset of the set of content segments isclassified into a type that is proposed not to be altered; determine asuggested replacement for the first subset of the set of contentsegments; alter the first subset of the set of content segments based atleast in part on the suggested replacement to generate a modifieddocument; and provide the modified document based on altering the firstsubset of the set of content segments.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions. The one or more instructions,when executed by one or more processors of a device, may cause the oneor more processors to: obtain a training data set including a pluralityof documents; train, using the training data set and a machine learningtechnique, a content classification model; store the contentclassification model for use in classifying a document; receive, afterstoring the content classification model, the document; parse thedocument to identify a set of content segments in the document;determine respective a set of contexts for the set of content segments;classify the set of content segments using the stored contentclassification model, wherein a first subset of the set of contentsegments is classified into a first type that is proposed to be alteredand a second subset of the set of content segments is classified into asecond type that is proposed not to be altered; alter the first subsetof the set of content segments based at least in part on the contentclassification model and the set of contexts, to generate a modifieddocument; provide the modified document based on altering the firstsubset of the set of content segments; update the stored contentclassification model based on one or more alterations to the firstsubset of the set of content segments; and store the updated contentclassification model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D are diagrams of an example implementation described herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes for document contentclassification and alteration.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A document may include sensitive information, which may include privateinformation, proprietary information, and/or other types of information,that is not to be provided to one or more recipients of the document.For example, a medical document may include private patient informationthat is viewable by a patient, a patient's healthcare provider, and/orthe like, but is not to be disclosed to third parties. As anotherexample, a legal document may include information that is viewable by aclient of an attorney, the attorney, and/or the like, but is not to bedisclosed to the public.

Documents may include sensitive information other than textualinformation. For example, an image may include some information that ispermitted to be provided to all viewers of the image and someinformation that is not permitted to be provided to at least a subset ofviewers. As an example, a map provider may intend to provide aphotograph of a house as part of a street-mapping functionality, but mayneed to obscure other objects in the photograph, such as vehicle licenseplates, faces, and/or the like. In some cases, documents may have mixedprivacy restrictions. For example, a particular document may includeembedded documents that have different privacy restrictions than theparticular document, different privacy restrictions than each other,and/or the like.

A device may redact or otherwise obscure information in a document. Forexample, a device may use a find-and-replace technique to find a set ofwords in a document and delete or replace the set of words. For example,a device may be configured to search for a patient's real name (e.g.,using a pre-configured list of patient names), and replace the patient'sreal name with a generic name (e.g., that is specified in thepre-configured list or is included in another list identifyingreplacement terms).

However, using a static find-and-replace technique may result in failureto obscure or replace non-textual information, such as images, audioclips, video clips, and/or the like that may include sensitiveinformation. Moreover, when there is an error in a document, the errormay result in the device failing to recognize information for redaction.For example, when a patient's name is incorrectly listed (e.g., as aresult of a typo, an error in optical character recognition, and/or thelike), the device may fail to find the patient's name. As a result,identifying information may remain in a document after completing afind-and-replace based document sanitization procedure.

Furthermore, some information may be private information in a firstcontext but public information in a second context. For example, whenredacting an address, ‘Java’ may be private information when ‘Java’refers to the island, but may be public information when ‘Java’ refersto the programming language or type of coffee. As another example,‘Washington’ may be private information when ‘Washington’ refers to anaddress, but may be public information when ‘Washington’ refers to thehistorical person, is a metonym for, for example, the U.S. government,and/or the like. Another issue with find-and-replace based documentsanitization is that redaction of information may result in aninformation content of a document falling below a threshold. Forexample, when a threshold amount of a document is redacted, the documentmay lack usefulness to a reader.

Some implementations described herein provide context-based documentcontent classification and alteration. For example, a documentprocessing platform may analyze a document, an embedded document withinthe document, and/or the like to identify content segments, such asimages, textual segments, videos, audio clips, and/or the like. Thedocument processing platform may determine a context for a contentsegment and may classify the content segment based on the context. Forexample, the document processing platform may determine a semanticmeaning of ‘Java’ in a document based on information in the document andmay determine whether to classify ‘Java’ as sensitive informationrelating to a person's address or non-sensitive information describing aprogramming language used for a software development project.

In this case, the document processing platform may automatically modifythe document to redact, alter, or obscure sensitive information. Forexample, the document processing platform may replace ‘Java’ with‘Private Address’ in the context of a person's address. In this case,the document processing platform may modify the document based on thecontext, thereby preserving an information content of the document(e.g., by automatically indicating that the redacted content segment wasan address, thereby avoiding confusion regarding what was redacted). Inthis way, the document processing platform reduces a likelihood that adocument becomes unusable as a result of excessive static redaction.

Additionally, or alternatively, for an image content segment, thedocument processing platform may recognize a person's face in an imageand automatically obscure a portion of the image including the person'sface. Additionally, or alternatively, based on results of processingand/or modifying the document, the document processing platform mayupdate and improve a content classification model. For example, thedocument processing platform may update the content classification modelto enable subsequent classification of a document segment and/orrecommendation of a modification to the document segment to be improved.In this way, the document processing platform uses machine learning tofurther improve content classification for subsequent documents andobviates a need for generation of a static list associated with afind-and-replace technique.

In this way, the document processing platform improves an accuracy ofdocument sanitization procedures relative to a static find-and-replacetechnique. Moreover, by improving an accuracy of document sanitization,the document processing platform obviates a need for manual review ofautomatic document sanitization and/or a need for manual documentsanitization, thereby reducing a utilization of computing resources.Furthermore, the document processing platform may enforce a set ofpermissions in real-time, thereby enabling real-time informationsecurity for documents.

FIGS. 1A-1D are diagrams of an example implementation 100 describedherein. As shown in FIG. 1A, example implementation 100 includes adocument processing platform 102.

As further shown in FIG. 1A, and by reference number 150, documentprocessing platform 102 may obtain training data. For example, documentprocessing platform 102 may obtain one or more classification lists,document data identifying contents of a set of documents, entity dataidentifying information regarding one or more organizations, and/or thelike. In some implementations, document processing platform 102 mayobtain training data identifying a blacklist of terms. For example,document processing platform 102 may determine a set of terms that areto be redacted, obscured, or replaced in documents that are to beprocessed. In this case, document processing platform 102 may use theblacklist as data for training a content classification model to usecontext to determine whether a blacklisted term is associated with afirst context, in which the blacklisted term is to be redacted, or asecond context, in which the blacklisted term is not to be redacted. Asan example, a blacklist may include one or more trademarked names,which, when used as generic names, may be omitted from redaction despitebeing on the blacklist. In this way, document processing platform 102improves document sanitization relative to using a blacklist for afind-and-replace procedure, in which context is not used.

Additionally, or alternatively, document processing platform 102 mayobtain a whitelist of terms. In this case, the whitelist may include alist of generic terms that are not to be redacted, but may be redactedwhen document processing platform 102 determines that, in context, aterm is not a generic term, but instead a part of an address.Additionally, or alternatively, document processing platform 102 mayobtain a blacklist, a whitelist, and/or the like of other content. Forexample, document processing platform 102 may obtain a whitelist oficons that are to be preserved in a document, such as generic icons thatare not to be classified as privileged information. In someimplementations, document processing platform 102 may obtain aclassification list (e.g., a blacklist, a whitelist, and/or the like)from a particular source. For example, document processing platform 102may obtain the classification list from a data structure storing namesof clients of an organization, and may determine that the classificationlist is a blacklist based on permission data indicating that names ofclients of the organization is privileged information. In this case,document processing platform 102 may communicate with, for example, aserver storing information from which to generate a classification list,to determine a level of permission of the server, and may determine atype of the classification list based on the level of permission.

In some implementations, document processing platform 102 may train oneor more models without obtaining any pre-made classification list. Forexample, document processing platform 102 may generate a classificationlist by parsing a server to identify clients of an organization based ona file structure of the server. In this case, document processingplatform 102 may generate a classification list for use in training acontent classification model, thereby obviating a need for auser-created classification list.

In some implementations, document processing platform 102 may obtaindocument data identifying a set of processed documents. For example,document processing platform 102 may obtain document data identifying adocument for which a document sanitization procedure has been performedto remove, alter, or obscure privileged information in the document. Inthis case, document processing platform 102 may obtain informationidentifying an original version of the document, a sanitized version ofthe document, and/or the like to enable a determination of which contentin the document (e.g., which terms, images, audio clips, video clips,and/or the like) has been removed, altered, or obscured, one or morecontexts of the content in the document, and/or the like.

In some implementations, document processing platform 102 may receiveentity data regarding an organization for which document processing isto be performed. For example, document processing platform 102 mayobtain information identifying an industry of the organization, a set ofemployees of the organization, a set of tasks of the organization, a setof clients of the organization, a set of projects being or having beencompleted by the organization, and/or the like. Additionally, oralternatively, document processing platform 102 may obtain entity dataregarding one or more organizations similar to the organization. Forexample, document processing platform 102 may determine a similarityscore based on a similarity of industry, task, client, project, and/orthe like, and may obtain data regarding the one or more otherorganizations.

In this case, document processing platform 102 may use entity data toidentify one or more similar organizations, and may obtain document datarelating to the one or more similar organizations to use in generatingone or more models for processing documents of the organization, asdescribed in more detail herein. In this way, document processingplatform 102 reduces a utilization of processing resources to generate amodel, network resources to obtain data for a model, memory resources tostore data for a model, and/or the like relative to obtaining allavailable document data for all available organizations. Moreover, basedon generating the one or more models using data relating to theorganization and/or similar organizations, document processing platform102 improves an accuracy of the model in processing documents of theorganization relative to including data associated with unrelatedorganizations that may have different data privacy rules, differentterminologies, and/or the like.

As further shown in FIG. 1A, and by reference number 152, documentprocessing platform 102 may generate a content classification model, anentity recognition model, and/or the like. For example, documentprocessing platform 102 may generate the content classification model toenable classification of content into a type of content that is to bealtered, redacted, or obscured, a type of content that is to remainunaltered, unredacted, or unobscured, a sub-class thereof, another typeof content, and/or the like. In this way, document processing platform102 trains a model to use contextual information to perform documentsanitization, thereby improving document sanitization relative tofind-and-replace techniques. In some implementations, documentprocessing platform 102 may obtain the content classification model fromanother source and use the content classification model based onobtaining the content classification model.

In some implementations, document processing platform 102 may generatethe content classification model using a machine learning technique. Forexample, document processing platform 102 may divide the document data,the classification lists, and/or the like into a training set, averification set, and/or the like. In this case, document processingplatform 102 may use the training set to train the contentclassification model to classify whether textual content, image content,audio content, video content, and/or the like is to be assigned to aparticular type based on a semantic meaning determined using contextualinformation. Additionally, or alternatively, document processingplatform 102 may train the content classification model to enable adetermination of a type of modification to use on a particular sub-typeof content. For example, document processing platform 102 may usedocument data indicating that a first term is redacted and a second termis replaced with a third term, to train the content classification modelto classify the first term and/or similar terms as a sub-type that is tobe redacted and to classify the second term and/or similar terms as asub-type that is to be replaced with a third term and/or similar terms.In some implementations, document processing platform 102 may use anopen source model rather than generating a model. For example, documentprocessing platform 102 may use a DBpedia model and/or the like.

In some implementations, document processing platform 102 may train anentity recognition model. For example, document processing platform 102may train the entity recognition model using document data,classification lists, and/or the like to identify entities, such asorganizations, persons, locations, and/or the like within documents. Inthis case, document processing platform 102 may use the entityrecognition model as a part of the content classification model, whichmay determine whether a particular recognized entity is to be modifiedor is to remain un-modified. As an example, document processing platform102 may use the entity recognition model to identify, in a document, aname of an organization that generated the document, and a name of aclient of the organization in the document. In this case, documentprocessing platform 102 may use the content classification model todetermine that the name of the organization is to remain in thedocument, but the name of the client is to be modified in the documentin order to sanitize the document.

In some implementations, document processing platform 102 may train thecontent classification model using one or more other data sets. Forexample, document processing platform 102 may train the contentclassification model using a dictionary, a thesaurus (e.g., a publishedthesaurus, a generated thesaurus based on document data, a generatedthesaurus based on a word-to-vector model, and/or the like), anencyclopedia, a set of regular expressions, a taxonomy of companyfunctions, a taxonomy of company projects, and/or the like to enable adetermination of a context of a content segment, and a classification ofthe content segment into a particular class (e.g., whether the contentsegment is sensitive information or non-sensitive information).

As further shown in FIG. 1A, and by reference number 154, documentprocessing platform 102 may store one or more models for subsequent usein processing a document. For example, document processing platform 102may store the content classification model, the entity recognitionmodel, and/or the like for subsequent use in processing documents.Additionally, or alternatively, document processing platform 102 mayprovide the one or more models to one or more client devices for localuse in processing documents.

As shown in FIG. 1B, and by reference number 156, document processingplatform 102 may receive a document. For example, document processingplatform 102 may receive a document for processing and documentsanitization. In some implementations, document processing platform 102may receive a document that includes a particular type of content. Forexample, document processing platform 102 may receive a document thatincludes textual content, image content, audio content, video content,and/or the like. Additionally, or alternatively, document processingplatform 102 may receive a document that includes multiple types ofcontent. For example, document processing platform 102 may receive adocument that includes textual content and image content.

In some implementations, document processing platform 102 may receive aparticular type of document. For example, document processing platform102 may receive a word processing document (e.g., a ‘.doc’, ‘.docx’,‘.msg’, ‘.txt’, and/or the like), an image editing document (e.g., a‘.img’, a ‘.jpg’, and/or the like), a spreadsheet document (e.g., a‘.xls’, ‘.xlsx’, ‘.xlsm’, ‘.xltx’, and/or the like), a portable documentformat (e.g., a ‘.pdf’) document, a compressed document (e.g., a ‘.zip’)file, a presentation document (e.g., a ‘.ppt’, ‘.pptx’, and/or thelike), an extensible markup language (e.g., a ‘.xml’) document, a codedocument (e.g., in a particular code language, such as a ‘.java’,‘.c++’, ‘.obj’, ‘.class’, and/or the like), an audio document (e.g., a‘.mp3’ or ‘.wav’ file), a video document (e.g., a ‘.mp4’ or ‘.mpeg’file), and/or the like. In some implementations, document processingplatform 102 may receive a document that includes one or more otherdocuments. For example, document processing platform 102 may receive adocument that includes a set of embedded documents, a set of linkeddocuments, and/or the like. In this case, document processing platform102 may process the set of embedded documents, the set of linkeddocuments, and/or the like to extract content stored therein, asdescribed in more detail herein. Additionally, or alternatively,document processing platform 102 may extract document properties ascontent segments for selective modification, document backgrounds ascontent segments for selective modification, and/or the like.

In some implementations, document processing platform 102 may monitor adocument. For example, document processing platform 102 may monitor aclient device on which a document is being dynamically generated (e.g.,by user input, using an algorithm, such as a speech-to-text algorithm togenerate a transcript of an audio clip, and/or the like), and maycontinuously or periodically process the dynamically generated documentto sanitize the dynamically generated document. In some implementations,document processing platform 102 may monitor an information stream. Forexample, document processing platform 102 may monitor a website, a setof email communications, a set of chat communications and/or the like todetermine whether a document, an update to a document, a communication,and/or the like includes sensitive information, and redact the sensitiveinformation in real-time.

In some implementations, document processing platform 102 may obtainother information relating to the document. For example, documentprocessing platform 102 may receive an indication of a user thatprovided the document, a project to which the document relates, a typeof the document, a context for the document, a blacklist or whitelistfor the document, and/or the like. In this way, document processingplatform 102 may customize document sanitization to the document,thereby improving document sanitization relative to staticfind-and-replace.

As further shown in FIG. 1B, and by reference number 158, documentprocessing platform 102 may process the document to determinealterations to the document. For example, as shown by reference numbers160, 162, and 164, document processing platform 102 may extract contentsegments, identify contexts for the content segments, classify thecontent segments, and/or the like. In some implementations, documentprocessing platform 102 may use a particular set of techniques toprocess the document. For example, to extract content segments, documentprocessing platform 102 may use a natural language processing technique.In this case, document processing platform 102 may identify individualwords, multi-word phrases, sentences, paragraphs, and/or the like asindividual content segments. Additionally, or alternatively, for audioclips, document processing platform 102 may convert the audio clips totext using a speech-to-text functionality, and may extract textualcontent segments based on converting the audio clips to text.Additionally, or alternatively, document processing platform 102 mayextract different portions of an audio clip, such as differentfrequencies, different repeating sounds, and/or the like as audiocontent segments. Additionally, or alternatively, document processingplatform 102 may extract images as content segments, portions of imagesthat include objects (e.g., using an object recognition and/or imageprocessing technique) as content segments, and/or the like.

Based on extracting content segments, document processing platform 102may identify contexts for the content segments, in some implementations.For example, document processing platform 102 may determine, using thecontent classification model, a semantic meaning of a word based onother words in the document (e.g., using a natural language processingtechnique). Additionally, or alternatively, document processing platform102 may determine whether a word corresponds to an entity, using anentity recognition model. Additionally, or alternatively, documentprocessing platform 102 may determine a semantic content of an image(e.g., based on words within a proximity to the image in the document,based on performing object recognition to determine objects within theimage), and/or the like. Additionally, or alternatively, documentprocessing platform 102 may determine a semantic content of an audioclip (e.g., words in the audio clip, a speaker of the audio clip, a typeof object making a sound in an audio clip, and/or the like).Additionally, or alternatively, document processing platform 102 maydetermine a semantic content of a video clip (e.g., objects in the videoclip, gestures being performed in the video clip, and/or the like).

In some implementations, document processing platform 102 may classifycontent segments into a type, a sub-type, and/or the like using thecontent classification model. For example, based on a context of atextual content segment, document processing platform 102 may classifythe textual content segment has having non-sensitive information that isnot to be replaced, obscured, or redacted. Additionally, oralternatively, document processing platform 102 may classify the textualcontent segment as having sensitive information (e.g., a name, an emailaddress, a client name, a monetary amount, a phone number, a locationidentifier, and/or the like), that is to be replaced, obscured, orredacted. In some implementations, document processing platform 102 mayclassify a portion of a content segment as sensitive information. Forexample, document processing platform 102 may classify a portion of animage (e.g., an icon providing sensitive information, such asinformation identifying a company, a person, an address, and/or thelike) as sensitive information to be replaced, obscured, or redacted.

In some implementations, document processing platform 102 may predict alikelihood that a content segment is a particular type. For example,document processing platform 102 may generate a score representing alikelihood that the content classification model has correctly assigneda content segment to a particular type or sub-type. In this case,document processing platform 102 may determine, based on the scoresatisfying a threshold, to provide a recommendation relating toclassifying the content segment to the particular type or sub-type. Insome implementations, document processing platform 102 may account forimperfect versions of a content segment. For example, documentprocessing platform 102 may determine the score for a textual contentsegment (e.g., that includes a typo) and based on the score satisfying athreshold, may predict that the textual content segment is of a typecorresponding to another version of the textual content segment (e.g.,without the typo). In this way, document processing platform 102 enablesdocument sanitization of documents that include errors, therebyimproving document sanitization relative to find-and-replace basedtechniques. Moreover, based on using a scoring to account for errors,document processing platform 102 obviates a need to include everypossible variant of, for example, a word in a find-and-replace list,thereby reducing an amount of data storage required to store afind-and-replace list that can successfully sanitize an error filleddocument.

As shown in FIG. 1C, and by reference number 166, document processingplatform 102 may provide information identifying recommendations of aset of alterations to the document. For example, document processingplatform 102 may provide a user interface 168 for display via a deviceto identify the recommendations. In some implementations, documentprocessing platform 102 may provide another document identifying the setof recommendations. For example, document processing platform 102 mayprovide a spreadsheet document identifying the set of recommendations toenable a user to view the set of recommendations. In this case, based ona user editing the spreadsheet document, document processing platform102 may determine which recommendations to implement and/or whichrecommendations are to be modified, as described in more detail herein.

In some implementations, document processing platform 102 may provideinformation identifying a content segment and a context for the contentsegment. For example, document processing platform 102 may provideinformation identifying a word or set of words (e.g., ‘Mom+Pop Co.’) anda determined context for the word or set of words (e.g., other words inproximity to the word or set of words, that the word or set of wordsidentifies a company name of a client of an organization). In this case,document processing platform 102 may provide a recommendation based onthe context (e.g., that the content segment be replaced with agenericized version of the content segment). In this case, documentprocessing platform 102 may select the recommendation to minimize a lossof semantic meaning. For example, rather than redacting the contentsegment (e.g., removing all semantic meaning) or using the context for areplacement of the content segment (e.g., replacing ‘Mom+Pop Co.’ with‘Company Name’, which removes a relatively large amount of semanticmeaning), document processing platform 102 may generate a genericversion of the content segment to preserve a relatively large amount ofsemantic meaning (e.g., ‘SodaCompany’, which provides more semanticmeaning than ‘Company Name’). In this way, document processing platform102 minimizes an amount of information loss during documentsanitization, thereby improving document sanitization relative toredaction of all sensitive information.

Additionally, or alternatively, document processing platform 102 mayprovide a recommendation relating to redacting, obscuring, or replacingimage content segments. For example, document processing platform 102may provide a recommendation to blur an image content segment (e.g., adetected logo), a portion of an image content segment (e.g., a detectedword, face, or object), and/or the like. In this way, documentprocessing platform 102 enables multimedia content to be sanitized in adocument sanitization procedure, thereby improving document sanitizationrelative to static find-and-replace based document sanitizationtechniques.

As further shown in FIG. 1C, and by reference number 170, based onproviding document processing user interface 168, document processingplatform 102 may receive modifications to the recommendations. Forexample, based on receiving user input indicating a change to arecommendation (e.g., a decision to override a recommendation by notredacting a phone number or a decision to modify a recommendation byaltering a replacement term), document processing platform 102 mayupdate the modified document. In this case, document processing platform102 may store information identifying the user input for use in updatingthe content classification model, thereby enabling supervised machinelearning to continue to train the content classification model.

As shown in FIG. 1D, and by reference number 172, document processingplatform 102 may provide a modified document to client device 106 fordisplay. For example, document processing platform 102 may provide themodified document for display to one or more users of client device 106.In some implementations, document processing platform 102 mayautomatically perform an action, such as publishing the modifieddocument. For example, document processing platform 102 may make themodified document accessible on a website, on a server, on an intranet,and/or the like. In some implementations, document processing platform102 may provide different versions of the modified document to differentusers of different client devices 106. For example, document processingplatform 102 may provide a first version of the modified document, witha first set of content segments modified, to a first user with a firstpermission level, and a second version of the modified document with asecond set of content segments modified to a second user with a secondpermission level.

As further shown in FIG. 1D, and by reference number 174, documentprocessing platform 102 may update one or more models based on themodifications to the recommendations. For example, document processingplatform 102 may alter the content classification model based on a userselection of a different modification to a content segment, may add anentity to a data structure storing entities to identify in documents foralteration, and/or the like. In this way, an accuracy of subsequentdocument processing is improved relative to a static find-and-replacetechnique for document sanitization. In some implementations, documentprocessing platform 102 may populate a blacklist or a whitelist based onthe modifications to the recommendations. For example, documentprocessing platform 102 may generate a whitelist of terms for which arecommendation was rejected, thereby reducing a quantity of terms thatare to be processed using a content classification model during asubsequent document sanitization procedure. Additionally, oralternatively, document processing platform 102 may generate and/ormodify a taxonomy, a set of regular expressions, and/or the like basedon modifications to the recommendations, and may update the contentclassification model based on generating and/or modifying the taxonomy,the set of regular expressions, and/or the like. In someimplementations, document processing platform 102 may update the contentclassification model on a user-specific or organization-specific basis.For example, document processing platform 102 may customize the contentclassification model based on user preferences derived from usermodifications to the recommendations. In this case, document processingplatform 102 may provide, when processing a document, a firstrecommendation to a first user and a second, different recommendation toa second user based on respective user preferences. In this way,document processing platform 102 enables user-differentiation ofdocument sanitization, which may enable accounting for differences inuser-writing styles, differences in user-needs with regard to documentsanitization, and/or the like.

As indicated above, FIGS. 1A-1D are provided merely as one or moreexamples. Other examples may differ from what is described with regardto FIGS. 1A-1D.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2,environment 200 may include a client device 210, a document processingplatform 220, a computing resource 225, a cloud computing environment230, and a network 240. Devices of environment 200 may interconnect viawired connections, wireless connections, or a combination of wired andwireless connections.

Client device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith document sanitization. For example, client device 210 may include acommunication and/or computing device, such as a mobile phone (e.g., asmart phone, a radiotelephone, etc.), a laptop computer, a tabletcomputer, a handheld computer, a desktop computer, a gaming device, awearable communication device (e.g., a smart wristwatch, a pair of smarteyeglasses, etc.), or a similar type of device.

Document processing platform 220 includes one or more computingresources assigned to sanitize a document. For example, documentprocessing platform 220 may be a platform implemented by cloud computingenvironment 230 that may sanitize a document. In some implementations,document processing platform 220 is implemented by computing resources225 of cloud computing environment 230.

Document processing platform 220 may include a server device or a groupof server devices. In some implementations, document processing platform220 may be hosted in cloud computing environment 230. Notably, whileimplementations described herein may describe document processingplatform 220 as being hosted in cloud computing environment 230, in someimplementations, document processing platform 220 may be non-cloud-basedor may be partially cloud-based.

Cloud computing environment 230 includes an environment that deliverscomputing as a service, whereby shared resources, services, and/or thelike may be provided to sanitize a document. Cloud computing environment230 may provide computation, software, data access, storage, and/orother services that do not require end-user knowledge of a physicallocation and configuration of a system and/or a device that delivers theservices. As shown, cloud computing environment 230 may include documentprocessing platform 220 and computing resource 225.

Computing resource 225 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource225 may host document processing platform 220. The cloud resources mayinclude compute instances executing in computing resource 225, storagedevices provided in computing resource 225, data transfer devicesprovided by computing resource 225, and/or the like. In someimplementations, computing resource 225 may communicate with othercomputing resources 225 via wired connections, wireless connections, ora combination of wired and wireless connections.

As further shown in FIG. 2, computing resource 225 may include a groupof cloud resources, such as one or more applications (“APPs”) 225-1, oneor more virtual machines (“VMs”) 225-2, virtualized storage (“VSs”)225-3, one or more hypervisors (“HYPs”) 225-4, or the like.

Application 225-1 includes one or more software applications that may beprovided to or accessed by client device 210. Application 225-1 mayeliminate a need to install and execute the software applications onclient device 210. For example, application 225-1 may include softwareassociated with document processing platform 220 and/or any othersoftware capable of being provided via cloud computing environment 230.In some implementations, one application 225-1 may send/receiveinformation to/from one or more other applications 225-1, via virtualmachine 225-2.

Virtual machine 225-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 225-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 225-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 225-2 may execute on behalf of a user(e.g., client device 210), and may manage infrastructure of cloudcomputing environment 230, such as data management, synchronization, orlong-duration data transfers.

Virtualized storage 225-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 225. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 225-4 provides hardware virtualization techniques that allowmultiple operating systems (e.g., “guest operating systems”) to executeconcurrently on a host computer, such as computing resource 225.Hypervisor 225-4 may present a virtual operating platform to the “guestoperating systems” and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 240 includes one or more wired and/or wireless networks. Forexample, network 240 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, and/or the like), a public land mobile network(PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the PublicSwitched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 2. Furthermore, two or more devices shown inFIG. 2 may be implemented within a single device, or a single deviceshown in FIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to client device 210, document processing platform 220,and/or computing resource 225. In some implementations, client device210, document processing platform 220, and/or computing resource 225 mayinclude one or more devices 300 and/or one or more components of device300. As shown in FIG. 3, device 300 may include a bus 310, a processor320, a memory 330, a storage component 340, an input component 350, anoutput component 360, and a communication interface 370.

Bus 310 includes a component that permits communication among multiplecomponents of device 300. Processor 320 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 320is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 360 includes a component thatprovides output information from device 300 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 300 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 370 may permit device300 to receive information from another device and/or provideinformation to another device. For example, communication interface 370may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for document contentclassification and alteration. In some implementations, one or moreprocess blocks of FIG. 4 may be performed by a document processingplatform (e.g., document processing platform 220). In someimplementations, one or more process blocks of FIG. 4 may be performedby another device or a group of devices separate from or including thedocument processing platform, such as a client device (e.g., clientdevice 210) and/or the like.

As shown in FIG. 4, process 400 may include receiving a documentincluding at least one of text, images, and one or more embeddeddocuments (block 405). For example, the document processing platform(e.g., using processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370 and/orthe like) may receive a document including at least one of text, images,and one or more embedded documents, as described above.

As further shown in FIG. 4, process 400 may include parsing the documentto identify a content segment in the document, wherein the contentsegment is at least one of a text segment or an image, from at least oneof the document or an embedded document of the one or more embeddeddocuments, of the document (block 410). For example, the documentprocessing platform (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370 and/or the like) may parse the document to identify acontent segment in the document, as described above. In someimplementations, the content segment is at least one of a text segmentor an image, from at least one of the document or an embedded documentof the one or more embedded documents, of the document.

As further shown in FIG. 4, process 400 may include determining acontext for the content segment, wherein the context includes at leastone of an entity identified by the content segment, a semantic meaningof the content segment, or an object identified by the content segment(block 415). For example, the document processing platform (e.g., usingprocessor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) maydetermine a context for the content segment, as described above. In someimplementations, the context includes at least one of an entityidentified by the content segment, a semantic meaning of the contentsegment, or an object identified by the content segment.

As further shown in FIG. 4, process 400 may include classifying thecontent segment using a content classification model and based on thecontext for the content segment, wherein the content segment isclassified into a first type that is proposed to be altered or a secondtype that is proposed not to be altered (block 420). For example, thedocument processing platform (e.g., using processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may classify the contentsegment using a content classification model and based on the contextfor the content segment, as described above. In some implementations,the content segment is classified into a first type that is proposed tobe altered or a second type that is proposed not to be altered.

As further shown in FIG. 4, process 400 may include determining asuggested replacement for the first type of content segment (block 425).For example, the document processing platform (e.g., using processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370 and/or the like) maydetermine a suggested replacement for the first type of content segment,as described above.

As further shown in FIG. 4, process 400 may include providing, fordisplay via a user interface, information identifying the first type ofcontent segment, the second type of content segment, and the suggestedreplacement (block 430). For example, the document processing platform(e.g., using processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370 and/orthe like) may provide, for display via a user interface, informationidentifying the first type of content segment, the second type ofcontent segment, and the suggested replacement, as described above.

As further shown in FIG. 4, process 400 may include receiving, via theuser interface, a set of selections of at least a portion of the firsttype of content segment or the second type of content segment or thesuggested replacement (block 435). For example, the document processingplatform (e.g., using processor 320, memory 330, storage component 340,input component 350, output component 360, communication interface 370and/or the like) may receive, via the user interface, a set ofselections of at least a portion of the first type of content segment orthe second type of content segment or the suggested replacement, asdescribed above.

As further shown in FIG. 4, process 400 may include selectively alteringthe content segment based on a set of alternation rules and the set ofselections to generate a modified document (block 440). For example, thedocument processing platform (e.g., using processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may selectively alter thecontent segment based on a set of alternation rules and the set ofselections to generate a modified document, as described above.

As further shown in FIG. 4, process 400 may include providing themodified document based on selectively altering the content segment(block 445). For example, the document processing platform (e.g., usingprocessor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) mayprovide the modified document based on selectively altering the contentsegment, as described above.

As further shown in FIG. 4, process 400 may include updating the contentclassification model based on the set of selections (block 450). Forexample, the document processing platform (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may update the contentclassification model based on the set of selections, as described above.

As further shown in FIG. 4, process 400 may include storing the updatedcontent classification model (block 455). For example, the documentprocessing platform (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370 and/or the like) may store the updated contentclassification model, as described above.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, process 400 includes obtaining a trainingdata set including a plurality of documents; training, using thetraining data set and a machine learning technique, the contentclassification model; and storing the content classification model foruse in classifying the document.

In a second implementation, alone or in combination with the firstimplementation, parsing the document includes identifying the one ormore embedded documents within the document; extracting one or morecontent segments from the one or more embedded documents, and includingthe one or more extracted content segments in a set of content segmentsfor classification and selective alteration.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, classifying the content segmentincludes comparing the content segment to at least one list to determinewhether to classify the content segment into the first type or thesecond type, and the at least one list includes at least one of ablacklist associated with the first type or a whitelist associated withthe second type.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, altering the content segmentincludes obscuring the content segment.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, altering the content segmentincludes identifying another content segment with another context, theother context and the context are associated with a threshold similarityscore, and replacing the content segment with the other content segmentin the modified document.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for document contentclassification and alteration. In some implementations, one or moreprocess blocks of FIG. 5 may be performed by a document processingplatform (e.g., document processing platform 20). In someimplementations, one or more process blocks of FIG. 5 may be performedby another device or a group of devices separate from or including thedocument processing platform, such as a client device (e.g., clientdevice 210) and/or the like.

As shown in FIG. 5, process 500 may include receiving a document (block510). For example, the document processing platform (e.g., usingprocessor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) mayreceive a document, as described above.

As further shown in FIG. 5, process 500 may include parsing the documentto identify a set of content segments in the document (block 520). Forexample, the document processing platform (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may parse the documentto identify a set of content segments in the document, as describedabove.

As further shown in FIG. 5, process 500 may include determining a set ofcontexts for the set of content segments (block 530). For example, thedocument processing platform (e.g., using processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may determine a set ofcontexts for the set of content segments, as described above.

As further shown in FIG. 5, process 500 may include classifying the setof content segments using a content classification model and based onthe set of contexts for the set of content segments, wherein a firstsubset of the set of content segments is classified into a type that isproposed to be altered, and a second subset of the set of contentsegments is classified into a type that is proposed not to be altered(block 540). For example, the document processing platform (e.g., usingprocessor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) mayclassify the set of content segments using a content classificationmodel and based on the set of contexts for the set of content segments,as described above. In some implementations, a first subset of the setof content segments is classified into a type that is proposed to bealtered, and a second subset of the set of content segments isclassified into a type that is proposed not to be altered.

As further shown in FIG. 5, process 500 may include determining asuggested replacement for the first subset of the set of contentsegments (block 550). For example, the document processing platform(e.g., using processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370 and/orthe like) may determine a suggested replacement for the first subset ofthe set of content segments, as described above.

As further shown in FIG. 5, process 500 may include altering the firstsubset of the set of content segments based at least in part on thesuggested replacement to generate a modified document (block 560). Forexample, the document processing platform (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may alter the firstsubset of the set of content segments based at least in part on thesuggested replacement to generate a modified document, as describedabove.

As further shown in FIG. 5, process 500 may include providing themodified document based on altering the first subset of the set ofcontent segments (block 570). For example, the document processingplatform (e.g., using processor 320, memory 330, storage component 340,input component 350, output component 360, communication interface 370and/or the like) may provide the modified document based on altering thefirst subset of the set of content segments, as described above.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, process 500 includes identifying, for a firstcontent segment of the first subset of the set of content segments witha first context, a second content segment with a second context, whereinthe first context is being a first semantic context and the secondcontext is a second semantic context, wherein the first semantic contextand the second semantic context is having a threshold semanticsimilarity score; and replacing the first content segment with thesecond content segment in the modified document.

In a second implementation, alone or in combination with the firstimplementation, process 500 includes removing a content segment of thefirst subset of the set of content segments from the modified document.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, process 500 includes obscuring acontent segment of the first subset of the set of content segments inthe modified document.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, process 500 includesclassifying a content segment as confidential information; and assigningthe content segment to the first subset of the set of content segmentsbased on classifying the content segment as confidential information.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, the document relates to aparticular industry and the set of contexts includes one or moreindustry-specific contexts associated with the particular industry.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, process 500 includesdetermining a risk score for a content segment based on a context forthe content segment; and assigning the content segment to the firstsubset of the set of content segments or the second subset of the set ofcontent segments based on the risk score.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for document contentclassification and alteration. In some implementations, one or moreprocess blocks of FIG. 6 may be performed by a document processingplatform (e.g., document processing platform 220). In someimplementations, one or more process blocks of FIG. 6 may be performedby another device or a group of devices separate from or including thedocument processing platform, such as a client device (e.g., clientdevice 210) and/or the like.

As shown in FIG. 6, process 600 may include obtaining a training dataset including a plurality of documents (block 605). For example, thedocument processing platform (e.g., using processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may obtain a training dataset including a plurality of documents, as described above.

As further shown in FIG. 6, process 600 may include training, using thetraining data set and a machine learning technique, a contentclassification model (block 610). For example, the document processingplatform (e.g., using processor 320, memory 330, storage component 340,input component 350, output component 360, communication interface 370and/or the like) may train, using the training data set and a machinelearning technique, a content classification model, as described above.

As further shown in FIG. 6, process 600 may include storing the contentclassification model for use in classifying a document (block 615). Forexample, the document processing platform (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may store the contentclassification model for use in classifying a document, as describedabove.

As further shown in FIG. 6, process 600 may include receiving, afterstoring the content classification model, the document (block 620). Forexample, the document processing platform (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may receive, afterstoring the content classification model, the document, as describedabove.

As further shown in FIG. 6, process 600 may include parsing the documentto identify a set of content segments in the document (block 625). Forexample, the document processing platform (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may parse the documentto identify a set of content segments in the document, as describedabove.

As further shown in FIG. 6, process 600 may include determiningrespective a set of contexts for the set of content segments (block630). For example, the document processing platform (e.g., usingprocessor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) maydetermine respective a set of contexts for the set of content segments,as described above.

As further shown in FIG. 6, process 600 may include classifying the setof content segments using the stored content classification model,wherein a first subset of the set of content segments is classified intoa first type that is proposed to be altered and a second subset of theset of content segments is classified into a second type that isproposed not to be altered (block 635). For example, the documentprocessing platform (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370 and/or the like) may classify the set of content segmentsusing the stored content classification model, as described above. Insome implementations, a first subset of the set of content segments isclassified into a first type that is proposed to be altered and a secondsubset of the set of content segments is classified into a second typethat is proposed not to be altered.

As further shown in FIG. 6, process 600 may include altering the firstsubset of the set of content segments based at least in part on thecontent classification model and the set of contexts, to generate amodified document (block 640). For example, the document processingplatform (e.g., using processor 320, memory 330, storage component 340,input component 350, output component 360, communication interface 370and/or the like) may alter the first subset of the set of contentsegments based at least in part on the content classification model andthe set of contexts, to generate a modified document, as describedabove.

As further shown in FIG. 6, process 600 may include providing themodified document based on altering the first subset of the set ofcontent segments (block 645). For example, the document processingplatform (e.g., using processor 320, memory 330, storage component 340,input component 350, output component 360, communication interface 370and/or the like) may provide the modified document based on altering thefirst subset of the set of content segments, as described above.

As further shown in FIG. 6, process 600 may include updating the storedcontent classification model based on one or more alterations to thefirst subset of the set of content segments (block 650). For example,the document processing platform (e.g., using processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may update the storedcontent classification model based on one or more alterations to thefirst subset of the set of content segments, as described above.

As further shown in FIG. 6, process 600 may include storing the updatedcontent classification model (block 655). For example, the documentprocessing platform (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370 and/or the like) may store the updated contentclassification model, as described above.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, process 600 includes determining a suggestedreplacement for the first type of content segment; providing, fordisplay via a user interface, information identifying the first type ofcontent segment, the second type of content segment, and the suggestedreplacement; and receiving, via the user interface, a set of selectionsof at least a portion of the first type of content segment or the secondtype of content segment or the suggested replacement.

In a second implementation, alone or in combination with the firstimplementation, process 600 includes automatically publishing thedocument to another device.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, a context, of the set of contexts,for a content segment, of the set of content segments, includesinformation identifying at least one of: a meaning of the contentsegment, an organization associated with the content segment, or acontact information associated with the content segment.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, process 600 includesclassifying a content segment of the set of content segments based on atleast one of: an industry of the content segment, or a synonym of thecontent segment.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, process 600 includesprocessing data of the document using an entity recognition model; andextracting the set of content segments is basing on a result ofprocessing the data of the document.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, the document is a dynamicdocument, and modify the dynamic document in real-time.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise forms disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, and/or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, or the like.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, and/or thelike. A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,etc.), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

1. A method, comprising: receiving, by a device, a document including atleast one of text, images, and one or more embedded documents; parsing,by the device, the document to identify a content segment in thedocument, wherein the content segment is at least one of a text segmentor an image, from at least one of the document or an embedded documentof the one or more embedded documents, of the document; determining, bythe device, a context for the content segment based on a semanticmeaning determined using contextual information, classifying, by thedevice, the content segment using a content classification model andbased on the context for the content segment, wherein the contentsegment is classified into a first type that is proposed to be alteredor a second type that is proposed not to be altered; determining asuggested replacement for the first type of content segment; providing,for display via a user interface, information identifying the first typeof content segment, the second type of content segment, and thesuggested replacement; receiving, via the user interface, a set ofselections of at least a portion of the first type of content segment orthe second type of content segment or the suggested replacement;selectively altering, by the device, the content segment based at leastin part on a set of alteration rules, the semantic meaning, and the setof selections, to generate a modified document; and providing, by thedevice, the modified document based on selectively altering the contentsegment; updating the content classification model based on the set ofselections; and storing the updated content classification model.
 2. Themethod of claim 1, further comprising: obtaining a training data setincluding a plurality of documents; training, using the training dataset and a machine learning technique, the content classification model;and storing the content classification model for use in classifying thedocument.
 3. The method of claim 1, wherein parsing the documentcomprises: identifying the one or more embedded documents within thedocument; extracting one or more content segments from the one or moreembedded documents; and including the one or more extracted contentsegments in a set of content segments for classification and selectivealteration.
 4. The method of claim 1, wherein classifying the contentsegment comprises: comparing the content segment to at least one list todetermine whether to classify the content segment into the first type orthe second type, and wherein the at least one list includes at least oneof: a blacklist associated with the first type, or a whitelistassociated with the second type.
 5. The method of claim 1, whereinaltering the content segment comprises: obscuring the content segment.6. The method of claim 1, wherein altering the content segmentcomprises: identifying another content segment with another context,wherein the other context and the context are associated with athreshold similarity score; and replacing the content segment with theother content segment in the modified document.
 7. A device, comprising:one or more memories; and one or more processors communicatively coupledto the one or more memories, configured to: receive a document; parsethe document to identify a set of content segments in the document;determine a set of contexts, based on a semantic meaning determinedusing contextual information, for the set of content segments; classifythe set of content segments using a content classification model andbased on the set of contexts for the set of content segments, wherein afirst subset of the set of content segments is classified into a typethat is proposed to be altered, and a second subset of the set ofcontent segments is classified into a type that is proposed not to bealtered; determine a suggested replacement based on the semantic meaningfor the first subset of the set of content segments; alter the firstsubset of the set of content segments based at least in part on thesuggested replacement to generate a modified document; and provide themodified document based on altering the first subset of the set ofcontent segments.
 8. The device of claim 7, wherein the one or moreprocessors are further configured to: identify, for a first contentsegment of the first subset of the set of content segments with a firstcontext, a second content segment with a second context, wherein thefirst context is a first semantic context and the second context is asecond semantic context, wherein the first semantic context and thesecond semantic context have a threshold semantic similarity score; andreplacing the first content segment with the second content segment inthe modified document.
 9. The device of claim 7, wherein the one or moreprocessors, when altering the first subset of the set of contentsegments, are configured to: remove a content segment of the firstsubset of the set of content segments from the modified document. 10.The device of claim 7, wherein the one or more processors, when alteringthe first subset of the set of content segments, are configured to:obscure a content segment of the first subset of the set of contentsegments in the modified document.
 11. The device of claim 7, whereinthe one or more processors, when classifying the set of contentsegments, are configured to: classify a content segment as confidentialinformation; and assign the content segment to the first subset of theset of content segments based on classifying the content segment asconfidential information.
 12. The device of claim 7, wherein thedocument relates to a particular industry and the set of contextsincludes one or more industry-specific contexts associated with theparticular industry.
 13. The device of claim 7, wherein the one or moreprocessors, when classifying the set of content segments, are configuredto: determine a risk score for a content segment based on a context forthe content segment; and assign the content segment to the first subsetof the set of content segments or the second subset of the set ofcontent segments based on the risk score.
 14. A non-transitorycomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors, cause the one or more processors to: obtain a training dataset including a plurality of documents; train, using the training dataset and a machine learning technique, a content classification model;store the content classification model for use in classifying adocument; receive, after storing the content classification model, thedocument; parse the document to identify a set of content segments inthe document; determine a respective-a set of contexts, based on asemantic meaning determined using contextual information, for the set ofcontent segments; classify the set of content segments using the storedcontent classification model, wherein a first subset of the set ofcontent segments is classified into a first type that is proposed to bealtered and a second subset of the set of content segments is classifiedinto a second type that is proposed not to be altered; alter the firstsubset of the set of content segments based at least in part on thecontent classification model and the semantic meaning, to generate amodified document; provide the modified document based on altering thefirst subset of the set of content segments; update the stored contentclassification model based on one or more alterations to the firstsubset of the set of content segments; and store the updated contentclassification model.
 15. The non-transitory computer-readable medium ofclaim 14, wherein the one or more instructions, when executed by the oneor more processors, further cause the one or more processors to:determine a suggested replacement for the first type of content segment;provide, for display via a user interface, information identifying thefirst type of content segment, the second type of content segment, andthe suggested replacement; and receive, via the user interface, a set ofselections of at least a portion of the first type of content segment orthe second type of content segment or the suggested replacement.
 16. Thenon-transitory computer-readable medium of claim 14, wherein the one ormore instructions, that cause the one or more processors to provide thedocument, cause the one or more processors to: automatically publish thedocument to another device.
 17. The non-transitory computer-readablemedium of claim 14, wherein a context, of the set of contexts, for acontent segment, of the set of content segments, includes informationidentifying at least one of: a meaning of the content segment, anorganization associated with the content segment, or a contactinformation associated with the content segment.
 18. The non-transitorycomputer-readable medium of claim 14, wherein the one or moreinstructions, that cause the one or more processors to classify the setof content segments, cause the one or more processors to: classify acontent segment of the set of content segments based on at least one of:an industry of the content segment, or a synonym of the content segment.19. The non-transitory computer-readable medium of claim 14, wherein theone or more instructions, that cause the one or more processors to parsethe document, cause the one or more processors to: process data of thedocument using an entity recognition model; and extract the set ofcontent segments based on a result of processing the data of thedocument.
 20. The non-transitory computer-readable medium of claim 14,wherein the document is a dynamic document; and wherein the one or moreinstructions, that cause the one or more processors to provide themodified document, cause the one or more processors to: modify thedynamic document in real-time.